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name: variant-pathogenicity-predictor description: Integrate REVEL, CADD, PolyPhen scores to predict variant pathogenicity version: 1.0.0 category: Bioinfo tags: [] author: AIPOCH license: MIT status: Draft risk_level: Medium skill_type: Tool/Script owner: AIPOCH reviewer: '' last_updated: '2026-02-06'

Variant Pathogenicity Predictor

Integrate REVEL, CADD, PolyPhen and other scores to predict variant pathogenicity.

Usage

python scripts/main.py --variant "chr17:43094692:G:A" --gene "BRCA1"
python scripts/main.py --vcf variants.vcf --output report.json

Parameters

• --variant: Variant in format chr:pos:ref:alt
• --vcf: VCF file with variants
• --gene: Gene symbol
• --scores: Prediction scores to use (REVEL,CADD,PolyPhen)

Integrated Scores

• REVEL (Rare Exome Variant Ensemble Learner)
• CADD (Combined Annotation Dependent Depletion)
• PolyPhen-2 (Polymorphism Phenotyping)
• SIFT (Sorting Intolerant From Tolerant)
• MutationTaster

Output

• Pathogenicity classification
• ACMG guideline interpretation
• Individual score breakdown
• Confidence assessment

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python/R scripts executed locally	Medium
Network Access	No external API calls	Low
File System Access	Read input files, write output files	Medium
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

No additional Python packages required.

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support